Geotechnical Investigation of Subaqueous Sediment Dynamics: A Case Study from the Arctic
Bibliographic record
Abstract
Subaqueous sediment dynamics including sediment erosion and deposition processes are directly related to sediment properties. In current sediment dynamics models and predictions, these sediment properties are often represented by the median grain size while other geotechnical parameters such as density, sediment strength, cohesion and pore water behavior are neglected. This results from existing uncertainties and gaps in knowledge about how geotechnical sediment characteristics change with ongoing sediment remobilization processes and vice versa. Potential consequences are uncertainties regarding the assessment of erodibility, sediment settling, consolidation and overall sediment stability. This impacts particularly current scientific, societal and engineering issues such as coastline protection and conservation, the resilience and sustainability of coastal infrastructure and communities, as well as the development of offshore and ocean renewable energy. After a brief overview of current research questions and developments, this presentation will feature results from a case study on coastal erosion in the Arctic. Herschel Island in the Yukon Territory, Canada, is located approximately 200 miles north of the polar circle, and suffers currently from permafrost retreat, some of the largest mud slumps in the world, and coastline erosion documented in the order of meters per year. In the framework of the international research expedition YUKON14 in 2014, geotechnical field tests were conducted in the nearshore zone of Herschel Island. The results indicated strong spatial heterogeneity of sediment distributions from gravelly to fine muddy sediments, vertical sediment stratification in the upper meter of the seabed surface including weak layers, and a variety of sub- and suprahydrostatic pore pressures associated to the respective stratification. This suggested a complex sediment transport and deposition pattern in the region, particularly under influence of (i) large sediment mass deposition events in response to the adjacent mud slumps and (ii) seasonal freeze-thaw cycles. Presenter Bio Nina received a Diploma (M.Sc.) in Geophysics in 2007 from the Westphalian Wilhelms University of Muenster, Germany. For her thesis, Characterization of seafloor sediments using eXpendable Bottom Penetrometer, she collaborated with Dr. Thomas Wever from the Institute for Underwater Acoustics and Geophysics of the German Navy in Kiel. She finished her Ph.D. in Marine Geotechnics in 2011 at MARUM-Center for Marine and Environmental Sciences at the University of Bremen, Germany. She worked on the “Geotechnical investigation of sediment remobilization processes using dynamic penetrometers” under the supervision of Prof. Achim Kopf. She was a postdoctoral fellow in 2011 in the marine geotechnics group at MARUM under supervision of Prof. Achim Kopf and, from 2012-2013, in the physical oceanography group at Dalhousie University, Halifax, Canada, under the supervision of Dr. Alex Hay, before being appointed assistant professor in the geotechnical engineering group of the Department of Civil and Environmental Engineering at Virginia Tech in Fall 2013. Her research focuses on coastal and marine geotechnics including instrument development, the geotechnical investigation of subaqueous sediment dynamics, beach dynamics, offshore renewable energies and navigation channel deepening and maintenance. She developed the free-fall penetrometer “Nimrod” in the framework of her Ph.D. and collaborated with BlueCDesigns on the design of “BlueDrop.” To date she has conducted more than 50 field surveys in the North Sea, Baltic Sea, Northern Atlantic, Arctic, Northern, Central and Southern Pacific, as well as in a number of lakes and rivers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".